Volume 10, Issue 2 (March & April 2019)                   BCN 2019, 10(2): 147-156 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Nazari A, Alavimajd H, Shakeri N, Bakhshandeh M, Faghihzadeh E, Marzbani H. Prediction of Brain Connectivity Map in Resting-State fMRI Data Using Shrinkage Estimator. BCN. 2019; 10 (2) :147-156
URL: http://bcn.iums.ac.ir/article-1-1040-en.html
1- Department of Biostatistics, Faculty of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
2- Department of Radiology Technology, Faculty of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
3- Department of Biomedical Engineering and Medical Physics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Introduction: In recent years, brain functional connectivity studies are extended using the advanced statistical methods. Functional connectivity is identified by synchronous activation in a spatially distinct region of the brain in resting-state functional Magnetic Resonance Imaging (MRI) data. For this purpose there are several methods such as seed-based correlation analysis based on temporal correlation between different Regions of Interests (ROIs) or between brain’s voxels of prior seed.
Methods: In the current study, test-retest Resting State functional MRI (rs-fMRI) data of 21 healthy subjects were analyzed to predict second replication connectivity map using first replication data. A potential estimator is “raw estimator” that uses the first replication data from each subject to predict the second replication connectivity map of the same subject. The second estimator, “mean estimator” uses the average of all sample subjectschr('39') connectivity to estimate the correlation map. Shrinkage estimator is made by shrinking raw estimator towards the average connectivity map of all subjectschr('39') first replicate. Prediction performance of the second replication correlation map is evaluated by Mean Squared Error (MSE) criteria.
Results: By the employment of seed-based correlation analysis and choosing precentral gyrus as the ROI over 21 subjects in the study, on average MSE for raw, mean and shrinkage estimator were 0.2169, 0.1118, and 0.1103, respectively. Also, percent reduction of MSE for shrinkage and mean estimator in comparison with raw estimator is 49.14 and 48.45, respectively.
Conclusion: Shrinkage approach has the positive effect on the prediction of functional connectivity. When data has a large between session variability, prediction of connectivity map can be improved by shrinking towards population mean. 
Type of Study: Original | Subject: Computational Neuroscience
Received: 2017/10/7 | Accepted: 2018/02/27 | Published: 2019/03/1

1. Andersen, A. H., Gash, D. M., & Avison, M. J. (1999). Principal component analysis of the dynamic response measured by fMRI: A generalized linear systems framework. Magnetic Resonance Imaging, 17(6), 795-815. [DOI:10.1016/S0730-725X (99)00028-4] [DOI:10.1016/S0730-725X(99)00028-4]
2. Behroozi, M., Daliri, M. R., & Boyaci, H. (2011). Statistical analysis methods for the fMRI data. Basic and Clinical Neuroscience, 2(4), 67-74. [PMID] [PMCID]
3. Bellec, P., Chu, C., Chouinard-Decorte, F., Benhajali, Y., Margulies, D. S., & Craddock, R. C. (2017). The neuro bureau ADHD-200 preprocessed repository. NeuroImage, 144(Part B), 275-86. [DOI:10.1016/j.neuroimage.2016.06.034] [DOI:10.1016/j.neuroimage.2016.06.034]
4. Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo‐planar MRI. Magnetic Resonance in Medicine, 34(4), 537-41. [DOI:10.1002/mrm.1910340409] [DOI:10.1002/mrm.1910340409]
5. Borumandnia, N., Majd, H. A., Zayeri, F., Baghestani, A. R., Tabatabaee, M., & Faeghi, F. (2017). Human brain functional connectivity in Resting-State fMRI data across the range of weeks. Middle East Journal of Family Medicine, 7(10), 148. [DOI:10.5742/MEWFM.2017.93068] [DOI:10.5742/MEWFM.2017.93068]
6. Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. (2001). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping, 14(3), 140-51. [DOI:10.1002/hbm.1048] [DOI:10.1002/hbm.1048]
7. Carroll, R. J., Ruppert, D., Stefanski, L. A., & Crainiceanu, C. M. (2006). Measurement error in nonlinear models: A modern Perspective. Boca Raton, Florida: CRC Press. [DOI:10.1201/9781420010138] [DOI:10.1201/9781420010138]
8. Cohen, A. L., Fair, D. A., Dosenbach, N. U., Miezin, F. M., Dierker, D., Van Essen, D. C., et al. (2008). Defining functional areas in individual human brains using resting functional connectivity MRI. NeuroImage, 41(1), 45-57. [DOI:10.1016/j.neuroimage.2008.01.066] [PMCID] [DOI:10.1016/j.neuroimage.2008.01.066]
9. Cordes, D., Haughton, V., Carew, J. D., Arfanakis, K., & Maravilla, K. (2002). Hierarchical clustering to measure connectivity in fMRI resting-state data. Magnetic Resonance Imaging, 20(4), 305-17. [DOI:10.1016/S0730-725X (02)00503-9] [DOI:10.1016/S0730-725X(02)00503-9]
10. Cribben, I., Haraldsdottir, R., Atlas, L. Y., Wager, T. D., & Lindquist, M. A. (2012). Dynamic connectivity regression: determining state-related changes in brain connectivity. NeuroImage, 61(4), 907-20. [DOI:10.1016/j.neuroimage.2012.03.070] [PMCID] [DOI:10.1016/j.neuroimage.2012.03.070]
11. Daliri, M., & Behroozi, M. (2012). fMRI: Clinical and research applications. OMICS Journal of Radiology, 1, e112. [DOI:10.4172/2167-7964.1000e112] [DOI:10.4172/2167-7964.1000e112]
12. Daliri, M., & Behroozi, M. (2013). Advantages and disadvantages of Resting State functional connectivity Magnetic Resonance Imaging for clinical applications. OMICS Journal of Radiology, 3, e123. [DOI:10.4172/2167-7964.1000e123] [DOI:10.4172/2167-7964.1000e123]
13. Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102(27), 9673-8. [DOI:10.1073/pnas.0504136102] [PMCID] [DOI:10.1073/pnas.0504136102]
14. Ghaderi, A. H., Nazari, M. A., Shahrokhi, H., & Darooneh, A. H. (2017). Functional brain connectivity differences between different ADHD presentations: Impaired functional segregation in ADHD-combined presentation but not in ADHD-inattentive presentation. Basic and Clinical Neuroscience, 8(4), 267-78. [DOI:10.18869/nirp.bcn.8.4.267] [PMCID] [DOI:10.18869/nirp.bcn.8.4.267]
15. Haman, J., & Valenta, Z. (2013). Shrinkage approach for gene expression data analysis. European Journal of Biomedical Informatics, 9(3), 2-8. [DOI:10.24105/ejbi.2013.09.3.2]
16. James, W., & Stein, C. (1961). Estimation with quadratic loss. Paper presented at the 4th Berkeley symposium on mathematical statistics and probability. Berkeley, California, United States, June 20-30 July 1960.
17. Lehmann, E. L., & Casella, G. (2006). Theory of point estimation. Berlin: Springer Science & Business Media.
18. Maldjian, J. A., Laurienti, P. J., & Burdette, J. H. (2004). Precentral gyrus discrepancy in electronic versions of the Talairach atlas. NeuroImage, 21(1), 450-5. [DOI:10.1016/j.neuroimage.2003.09.032] [DOI:10.1016/j.neuroimage.2003.09.032]
19. Maldjian, J. A., Laurienti, P. J., Kraft, R. A., & Burdette, J. H. (2003). An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. NeuroImage, 19(3), 1233-9. [DOI:10.1016/S1053-8119 (03)00169-1] [DOI:10.1016/S1053-8119(03)00169-1]
20. McKeown, M. J., Makeig, S., Brown, G. G., Jung, T. P., Kindermann, S. S., Bell, A. J., et al. (1998). Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping, 6(3), 160-88. [DOI:10.1002/ (sici)1097-0193 (1998)6:3<160::aid-hbm5>3.3.co;2-r] https://doi.org/10.1002/(SICI)1097-0193(1998)6:3<160::AID-HBM5>3.0.CO;2-1 [DOI:10.1002/(SICI)1097-0193(1998)6:33.0.CO;2-1]
21. Mejia, A. F., Nebel, M. B., Shou, H., Crainiceanu, C. M., Pekar, J. J., Mostofsky, S., et al. (2015). Improving reliability of subject-level Resting-State fMRI parcellation with shrinkage estimators. NeuroImage, 112, 14-29. [DOI:10.1016/j.neuroimage.2015.02.042] [PMCID] [DOI:10.1016/j.neuroimage.2015.02.042]
22. Oishi, K., Faria, A., Jiang, H., Li, X., Akhter, K., Zhang, J., et al. (2009). Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: Application to normal elderly and Alzheimer's disease participants. NeuroImage, 46(2), 486-99. [DOI:10.1016/j.neuroimage.2009.01.002] [DOI:10.1016/j.neuroimage.2009.01.002]
23. Sadeghi, A. Z., Jafari, A. H., Oghabian, M. A., Salighehrad, H. R., Batouli, S. A. H., Raminfard, S., etal. (2017). Changes in effective connectivity network patterns in drug abusers, treated with different methods. Basic and Clinical Neuroscience, 8(4), 285-98. [DOI:10.18869/nirp.bcn.8.4.285] [DOI:10.18869/nirp.bcn.8.4.285]
24. Shou, H., Eloyan, A., Lee, S., Zipunnikov, V., Crainiceanu, A., Nebel, M., et al. (2013). Quantifying the reliability of image replication studies: The image intra-class correlation coefficient (I2C2). Cognitive, Affective & Behavioral Neuroscience, 13(4), 714-24. [DOI:10.3758/s13415-013-0196-0] [DOI:10.3758/s13415-013-0196-0]
25. Shou, H., Eloyan, A., Nebel, M. B., Mejia, A., Pekar, J. J., Mostofsky, S., ET AL. (2014). Shrinkage prediction of seed-voxel brain connectivity using resting state fMRI. NeuroImage, 102(2), 938-44. [DOI:10.1016/j.neuroimage.2014.05.043] [DOI:10.1016/j.neuroimage.2014.05.043]
26. Varoquaux, G., Gramfort, A., Poline, J.-B., & Thirion, B. (2010). Brain covariance selection: better individual functional connectivity models using population prior. Paper presented at the Advances in Neural Information Processing Systems, Vancouver, Canada, 6 December 2010.
27. Zhang, L., Guindani, M., & Vannucci, M. (2015). Bayesian models for fMRI data analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 7(1), 21-41. [DOI:10.1002/wics.1339] [DOI:10.1002/wics.1339]

Add your comments about this article : Your username or Email:

Send email to the article author

© 2020 All Rights Reserved | Basic and Clinical Neuroscience

Designed & Developed by : Yektaweb